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Changes in the Geographic Distribution of the Diana Fritillary (Speyeria diana: Nymphalidae) under Forecasted Predictions of Climate Change

Department of Biological Sciences, University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA
Department of Biology, University of Arkansas at Little Rock, 2801 South University Ave., Little Rock, AR 72204, USA
Author to whom correspondence should be addressed.
Insects 2018, 9(3), 94;
Submission received: 2 July 2018 / Revised: 20 July 2018 / Accepted: 27 July 2018 / Published: 2 August 2018
(This article belongs to the Special Issue Butterfly Ecology and Conservation)


Climate change is predicted to alter the geographic distribution of a wide variety of taxa, including butterfly species. Research has focused primarily on high latitude species in North America, with no known studies examining responses of taxa in the southeastern United States. The Diana fritillary (Speyeria diana) has experienced a recent range retraction in that region, disappearing from lowland sites and now persisting in two phylogenetically distinct high elevation populations. These findings are consistent with the predicted effects of a warming climate on numerous taxa, including other butterfly species in North America and Europe. We used ecological niche modeling to predict future changes to the distribution of S. diana under several climate models. To evaluate how climate change might influence the geographic distribution of this butterfly, we developed ecological niche models using Maxent. We used two global circulation models, the community climate system model (CCSM) and the model for interdisciplinary research on climate (MIROC), under low and high emissions scenarios to predict the future distribution of S. diana. Models were evaluated using the receiver operating characteristics area under curve (AUC) test and the true skill statistics (TSS) (mean AUC = 0.91 ± 0.0028 SE, TSS = 0.87 ± 0.0032 SE for representative concentration pathway (RCP) = 4.5; and mean AUC = 0.87 ± 0.0031 SE, TSS = 0.84 ± 0.0032 SE for RCP = 8.5), which both indicate that the models we produced were significantly better than random (0.5). The four modeled climate scenarios resulted in an average loss of 91% of suitable habitat for S. diana by 2050. Populations in the southern Appalachian Mountains were predicted to suffer the most severe fragmentation and reduction in suitable habitat, threatening an important source of genetic diversity for the species. The geographic and genetic isolation of populations in the west suggest that those populations are equally as vulnerable to decline in the future, warranting ongoing conservation of those populations as well. Our results suggest that the Diana fritillary is under threat of decline by 2050 across its entire distribution from climate change, and is likely to be negatively affected by other human-induced factors as well.

1. Introduction

Understanding how species distributions might shift with the changing climate is a critical component of managing and protecting future biodiversity. Hundreds of species in the United States and elsewhere have responded to the warming climate by shifting to higher latitudes or elevations [1,2,3,4]. Such range shifts have been documented in a number of taxa [5,6,7], including alpine plants [8], marine invertebrates [9], marine fish [10], mosquitoes [11], birds [12,13], and butterflies [1,14,15,16,17,18]. A number of species distribution models have been developed to predict the impacts of climate change on species distributions, including bioclimate envelope models, which are useful first estimates of the potential effects of climate change on altering species’ ranges [19]. Bioclimate envelope models work by identifying the climatic bounds within which a species currently occurs, and then delineating how those climatic bounds will shift under various future climate projections [20,21,22,23].
Most often, researchers are limited to presence-only occurrence data, requiring the use of indirect methods to infer a species’ climatic requirements [8,24,25]. One of the best performing models using presence-only data is maximum entropy modeling, or Maxent [26], which performs well even with low sample sizes typical of rare species [19,27,28]. Maxent works by comparing climate data from occurrence sites with those from a random sample of sites from the larger landscape to minimize the relative entropy of statistical models’ fit to each data set. Species distribution models such as Maxent have been criticized for being overly simplistic, because they do not incorporate external biotic factors such as species interactions [20,27,29]. However, such bioclimate envelope models have been used to project with reasonable accuracy whether species ranges will increase or decrease under a changing climate [19,30,31,32], which was the primary objective of this study.
Speyeria diana (Nymphalidae) (Cramer 1777) is a butterfly species endemic to the southeastern United States and is currently threatened across portions of its range. This species is of particular conservation interest because it has experienced a range collapse in recent decades resulting in an 800-km geographic and genetic disjunction between western populations in the Ouachita and Ozark Mountains and populations in the southern Appalachian Mountains, and has shifted to a higher elevation at an estimated rate of 18 m per decade [33]. This range contraction is consistent with the predicted effects of a warming climate, and might represent the first such documented case in the southeastern United States, though the region has experienced other environmental changes in recent decades as well [33]. Previous research using coalescent-based population divergence models dated the earliest splitting of the western population from the east at least 20,000 years ago, during the last glacial maximum [34]. In addition, recent geometric morphometric evidence from the wings of S. diana further support this long-term spatial and genetic isolation [35]. In light of these pieces of evidence, we used Maxent to model the future distribution of S. diana under several future climatic scenarios, in order to forecast how the range of the butterfly might shift under predicted conditions. Forecasts of large range reductions (over 50%), or small overlaps between current and future ranges (less than 50%), would suggest high vulnerability to climate change. Range reductions of any size in the western distribution would likely threaten those populations that are genetically isolated and adapted to relatively low dispersal, with the negative effects of genetic drift [34,35].

2. Methods

2.1. Study Species

The Diana fritillary, Speyeria diana, is a large and sexually dimorphic nymphalid butterfly, endemic to the southeastern United States. Adult males emerge in late May to early June, with females flying several weeks to a month later [36]. Once mated, each female can lay thousands of eggs singly on ground litter during the months of August and September in the vicinity of Viola spp., the larval host plant for all Speyeria [37]. After hatching, first instar larvae immediately burrow deep into the leaf litter layer of the forest floor, where they overwinter [38]. In spring, larvae feed on the foliage of freshly emerging violets. Adult Diana butterflies are often found along forest edges or dirt roads containing tall, conspicuous nectar sources such as milkweeds, butterfly bushes, or other large summer and fall composites [39,40,41,42]. While males begin to die off in late July, females may persist in large numbers, although somewhat cryptically, through October [42].

2.2. Distributional Dataset

We searched for all known records of S. diana, from publications, catalogued and uncatalogued specimens in public and private collections in the United States and Europe, online databases, contemporary field surveys by scientists and amateurs, and our own field surveys. We obtained distributional data from 1323 pinned S. diana specimens from 33 natural history museum collections in the United States and Europe (Table 1). Four hundred thirty-five additional records (1938–2012) were provided by the Butterfly and Moth Information Network and the participants who contribute to its BAMONA project. Our literature survey produced 153 records (1818–2011) across 54 U.S. counties (Table 2). We also collected 469 S. diana butterflies in our own field surveys (Table 3). Our dataset essentially represents a complete dataset of all publicly available records for the species, and is as comprehensive as for any taxon in the region [33]. For this reason, our dataset should be especially informative in creating an accurate bioclimate envelope for the species, as collection bias is a major consideration with ecological niche modeling [43,44].

2.3. Species Distributional Modeling

We developed species distribution models using the popular machine-learning algorithm for ecological modeling, Maxent [26]. Maxent estimates a species’ probability distribution that has maximum entropy (closest to uniform), subject to a set of constraints based on the sampling of presence-only data [45]. Because of the difficulty and impracticality of obtaining accurate absence data, presence-only data are most often used in species distribution modeling. In order to offset the lack of absence data, Maxent uses a background sample to compare the distribution of presence data along environmental gradients with the distribution of background points randomly drawn from the study area [46,47,48]. Locality data and the randomly sampled background points are combined with climatic data to predict the probability of the species’ occurrence within each raster grid cell. We used environmental climate data from WorldClim [49] at 30 arc-second resolution or approximately 1 km2 grid cells. Bioclimate variables and elevation layers were each clipped to the extent of North America using ESRI (Environmental Systems Research Institute) ArcMap 10.0, and data extracted to S. diana sample localities. Additionally, we collected the same types of locality data for three other species of North American butterflies (Speyeria cybele, Speyeria idalia, Battus philenor), which served as 5628 random background points for our models. We utilized these background data to minimize spatial bias in our modeling, as data represented by similar butterfly species can be used as pseudo-absence data with the same collection bias as our occurrence data, improving the accuracy of the model [50,51].
Climatic variables included 19 derived bioclimatic variables that describe annual and seasonal variation in temperature and precipitation, as well as elevation, averaged for 1950–2000 (Table 4). One concern when modeling species distributions is the strong correlation that occurs between multiple climate variables, which can significantly influence model predictions of species distributions [52]. To test for co-linearity, we performed spatial autocorrelation statistics between all pairs of the 19 bioclimate variables using ESRI ArcMap 10.0. We then selected the most biologically meaningful variable for each group of two or more variables with Pearson correlation coefficients higher than 0.7 (Table 4). This allowed us to reduce the number of bioclimate variables to the nine potentially most important ones, which were: Minimum Temperature of Coldest Month, Mean Temperature of Driest Quarter, Precipitation of Wettest Month, Precipitation of Driest Month, Precipitation of Driest Quarter, Isothermality, Mean Diurnal Range (Mean of monthly (maximum temperature—minimum temperature)), Temperature Annual Range, and Annual Precipitation, along with elevation (Table 4). These variables are typically considered to be important determinants of butterfly distributions, as they relate to life history traits. Butterflies are highly sensitive to weather and climate, particularly changes in temperature and rainfall [53]. For example, mean temperature of the coldest month is related to the overwintering survival of first instar larvae, growing degree days above 5 °C are regarded as a surrogate for the developmental threshold of the larvae, water balance corresponds to the moisture availability for the larval host and adult nectar plants, and the mean temperature of late summer ensures proper adult emergence and mating [54,55,56,57,58,59]. Temperature changes affect all aspects of butterfly life history, from their distribution and abundance [14,54], to their realized fecundity [60,61]. Changes in rainfall levels can influence butterfly larvae indirectly through changes in host plant quality, and generally rainfall is considered to be beneficial because it enhances host plant growth [62].
One concern when modeling species distributions is whether the occurrence records are spatially biased with respect to site accessibility (e.g., towns, roads, trails) [63]. To address this concern, we applied a spatial filter to remove all sampling points that were within 5 km of each other using ESRI ArcMap 10.0. The spatial filter resulted in 254 unique presence points for S. diana that were used in the final model. We first modeled the distribution of these 254 occurrences in present-day climate, and then projected the fitted species distribution under two future climate scenarios for the period 2040–2069 (hereafter referred to as 2050). Future climate scenarios were taken from two global circulation models (GCMs) obtained from; the community climate system model (CCSM) [64] and the model for interdisciplinary research on climate (MIROC) [65,66]. These GCMs differ in the reconstruction of several climatic variables and are well known to produce different outcomes for butterfly species [67,68]. For example, in hind-casting Mediterranean butterflies, the CCSM model projects narrower distributions at the last glacial maximum than does MIROC [65,66]. For each of these two GCMs, we considered two different representative concentration pathways (RCPs) [69,70,71,72,73], which are cumulative measures of human emissions of greenhouse gases from all sources expressed in Watts per square meter. These pathways were developed for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [67] and correspond to a total anthropogenic radiative forcing of RCP = 4.5 W/m2 (low) and RCP = 8.5 W/m2 (high) [72,73].
We used Maxent’s default parameters [26,50] and a ten-fold cross-validation approach to further reduce bias with respect to locality data. This method divides presence data into ten equal partitions, with nine used to train the model, and the tenth used to test it. These partitions generate ten maps (one map per run), with each raster grid cell containing a value representing the probability of occurrence. These values were used to designate habitat suitability ranging from 0 (unsuitable habitat) to 1 (highly suitable habitat) (Figure 1). We averaged the resulting maps for the current climate, and for the two GCMs under RCP = 4.5 and RCP = 8.5. This method resulted in the production of a “low” and “high” average prediction for S. diana species distribution in 2050, represented with habitat suitability maps. We measured the goodness of fit for the models using the area under the curve (AUC) of a receiver-operating characteristic (ROC) plot [74]. We used criteria of Swets [75] and considered AUC values higher than 0.7 representative of model predictions significantly better than random values of 0.5 or less [26,27,74]. Because AUC has been recognized as a somewhat questionable measure of accuracy, especially when used with background data instead of true absences [74,76], we also calculated the TSS (true skill statistics), a threshold-dependent evaluation metric [76,77]. The relative importance of each variable’s contribution was assessed by sequential variable removal by Jackknife [26].

3. Results

Species distributional modeling resulted in “excellent” model fits for Speyeria diana, with a mean AUC = 0.91 ± 0.0028 SE, TSS = 0.87 ± 0.0032 SE for RCP = 4.5; and a mean AUC = 0.87 ± 0.0031 SE, TSS = 0.84 ± 0.0032 SE for RCP = 8.5 (Table 1). Annual precipitation explained the largest fraction of the distribution of S. diana under both RCPs (17.9%, RCP = 4.5; 19.4%, RCP = 8.5). Among the remaining bioclimatic variables, mean temperature of driest quarter had the next highest average percent contribution (10.3%, RCP = 4.5; 25.0%, RCP = 8.5), followed by minimum temperature of coldest month (20.1%, RCP = 4.5; 10.4%, RCP = 8.5), isothermality (7.3%, RCP = 4.5; 7.6%, RCP = 8.5), precipitation of wettest month (3.5%, RCP = 4.5; 3.9%, RCP = 8.5), precipitation of driest month (1.4%, RCP = 4.5; 5.4%, RCP = 8.5), precipitation of driest quarter (3.3%, RCP = 4.5; 2.4%, RCP = 8.5), Elev (1.5%, RCP = 4.5; 3.5%, RCP = 8.5), mean diurnal range (1.8%, RCP = 4.5; 2.8%, RCP = 8.5), and temperature annual range (1.6%, RCP = 4.5; 1.3%, RCP = 8.5) (Table 1).
Modelling with Maxent under the selected climate-change scenarios predicted that habitat suitability would decrease for S. diana by 2050 (two-tailed paired t-tests comparing current Maxent values with those of 2050; all p < 0.01). The MIROC model resulted in more loss of suitable habitat than CCSM under both RCP scenarios (88.2% versus 92.4% of suitable habitat retained for RCP 4.5, and 90.2% versus 94.3% of suitable habitat retained for RCP 8.5 in CCSM and MIROC, respectively). Both climate models indicate that the loss of core distributional area is modest, with an average of 91.3% of present distributional areas retained. The most drastic reduction in habitat is apparent across the southern Appalachian Mountains (Figure 2).

4. Discussion

Our ecological niche models predicted that the amount of suitable habitat for Speyeria diana will decline substantially by the year 2050 across its entire distribution. Both CCSM and MIROC climate models predicted severe habitat loss and fragmentation in the southern Appalachian Mountains by 2050, with some range expansion predicted into higher latitudes in both eastern and western populations. High elevation habitat will be an important refuge for the species across the entire distribution, as the range of S. diana is already shifting to higher elevations at an estimated rate of 18 m per decade [33]. Recent evidence further suggests that some S. diana populations may already be adapting to high elevations, as S. diana female forewings from high elevation populations were found to be narrower than low elevation populations, indicating that these females may be more mobile than those from low elevations with wider forewings [35].
Unlike populations in the eastern distribution, the wing shape of western populations of S. diana appears to be better adapted for lower dispersal, which is in alignment with findings that western populations of S. diana are both spatially and genetically isolated [35]. Our models predicted that the southern edge of the highly suitable habitat in the west will recede by 2050; However, as was found in the southern Appalachian Mountains, the suitable habitat was predicted to expand in the higher elevations of the Ozark and Ouachita mountains of Arkansas. The genetic isolation of western populations may ultimately prevent them from adapting to higher elevations as successfully as populations in the eastern distribution of the species. If this is the case, lower elevation populations will be even more vulnerable to climate change than our models predict.
We would like to note that all ecological niche models should be used and interpreted with caution because of various sources of bias and error that result in inaccurate predictions [78]. Some have questioned the applicability of bioclimatic modeling at regional scales because of the somewhat coarse resolution [79]. However, we are confident that the size of our study area, and our uniquely extensive dataset, provide sufficient data to forecast climate-driven range shifts in S. diana with accuracy. Both global circulation models (CCCM and MIROC) were very closely aligned in their outcomes, indicating strong agreement between them. Climate is well understood to play a primary role in shaping the distributions of species [80], and we are confident in our overall findings that the suitable habitat for S. diana will decline and become increasingly fragmented by 2050.

5. Conclusions

These results highlight the importance of maintaining connectivity of the suitable habitat for S. diana, especially in the eastern populations that appear most vulnerable to increased fragmentation and loss of suitable habitat. These populations in the eastern distribution of S. diana harbor important genetic diversity that may become lost through genetic drift if these populations become small and isolated. The Ozark and Ouachita Mountains of Arkansas and Missouri appear to be least vulnerable to loss of suitable habitat from climate change, and therefore will be important for the future conservation of S. diana after 2050. As a result of the geographic and genetic isolation of the western populations, conservation of suitable habitat in the west is equally as important as in the east. Our climate models show that the 800-km disjunction across the center of the range of S. diana is not due to complete absence of suitable habitat, but more probably a result of the extensive habitat fragmentation regionally across the Ohio River Valley from agricultural land use change, and other human related factors that were not included in our models. We conclude that maintaining well-connected low and high elevation habitats across the entire distribution of S. diana, both now and into the future, will be necessary for this species, even under conservative forecasts of climate change.

Author Contributions

Conceptualization, D.T., C.N.W.; Methodology, C.N.W., D.T; Validation, C.N.W.; Formal Analysis, C.N.W.; Investigation, C.N.W.; Resources, C.N.W.; Data Curation, C.N.W, D.T.; Writing-Original Draft Preparation, C.N.W.; Writing-Review & Editing, D.T.; Visualization, C.N.W., D.T.; Supervision, D.T.; Project Administration, C.N.W.; Funding Acquisition, C.N.W., D.T.


This research received no external funding.


We would like to thank Kyle Barrett, and Sergio Marchant for their valuable assistance with this project. We also thank Peter Marko, Saara Dewalt, and Peter Adler for their careful reviews of this project and manuscript.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. The present-day geographic distribution of Speyeria diana, with indices of habitat suitability as predicted by maximum entropy modelling (Maxent) under current climatic conditions (1950–2010).
Figure 1. The present-day geographic distribution of Speyeria diana, with indices of habitat suitability as predicted by maximum entropy modelling (Maxent) under current climatic conditions (1950–2010).
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Figure 2. (a) Habitat suitability indices for the projected future distribution of Speyeria diana under the community climate system model (CCMA) and model for interdisciplinary research on climate (MIROC) representative concentration pathways (RCP) 4.5 climate change scenarios; (b) habitat suitability indices for the projected future distribution of Speyeria diana under the CCMA and MIROC RCP 8.5 climate change scenarios.
Figure 2. (a) Habitat suitability indices for the projected future distribution of Speyeria diana under the community climate system model (CCMA) and model for interdisciplinary research on climate (MIROC) representative concentration pathways (RCP) 4.5 climate change scenarios; (b) habitat suitability indices for the projected future distribution of Speyeria diana under the CCMA and MIROC RCP 8.5 climate change scenarios.
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Table 1. Summary of Speyeria diana distributional data sources (adapted from Wells and Tonkyn 2014).
Table 1. Summary of Speyeria diana distributional data sources (adapted from Wells and Tonkyn 2014).
National Museums (N. American)LocationNo. of S. dianaRange of Specimen DatesNo. of Counties
Carnegie Museum of Natural HistoryPittsburgh, Pennsylvania1421889–200026
National Museum of Natural HistoryWashington, DC1291907–200226
American Museum of Natural HistoryNew York, NY1041921–198528
The Field MuseumChicago, IL981889–199523
California Academy of SciencesSan Francisco, CA881886–200012
Georgia Museum of Natural HistoryAthens, GA151935–19878
Cleveland Museum of Natural HistoryCleveland, Ohio61921–19656
Denver Museum of Nature and ScienceDenver, Colorado41939–19733
Mount Magazine State ParkParis, Arkansas419971
National History Museums (European)
British Natural History MuseumLondon, UK311777–198917
Paris Muséum national d’Histoire naturelleParis, France818901
Oxford Museum of Natural HistoryOxford, UK41937–19714
Zoölogisch Museum AmsterdamAmsterdam, The Netherlands41884–19213
Naturalis Biodiversity CenterLeiden, Netherlands4
Royal Ontario MuseumOntario, Canada31933–19683
University Collections
University of FloridaGainesville, Florida4091900–200743
University of MichiganEast Lansing, Michigan661909–198513
Clemson UniversityClemson, South Carolina431926–19785
Peabody, Yale UniversityNew Haven, Connecticut291904–19618
University of MissouriColumbia, Missouri291886–19808
University of WyomingLaramie, Wyoming131955–19794
University of Arkansas, Little RockLittle Rock, Arkansas122005–20075
University of California, BerkleyBerkley, California121926–19816
University of NebraskaLincoln, Nebraska141954–20037
North Carolina State UniversityRaleigh, North Carolina101904–19649
University of Arkansas, FayettevilleFayetteville, Arkansas101977–19945
Virginia Polytechnic InstBlacksburg, Virginia81911–19771
Louisiana State UniversityBaton Rouge, Louisiana71984–19881
University of WisconsinMadison, WI51926–19512
College of CharlestonCharleston, South Carolina420082
West Virginia UniversityMorgontown, West Virginia31977–19952
Furman UniversityGreenville, South Carolina31929–19903
Dalton State CollegeDalton, Georgia220011
State Agencies, online databases, listserves, individuals, and organizations
Field Surveys 4691995–201246
Butterflies and Moths of America (BAMONA) 4351938–201239
North Carolina 19th Approximation ( 2761938–201131
West Virginia Divisions of Natural Resources ( 2041978–199911
Literature survey 1531818–201154
Kentucky Dept. of Fish and Wildlife Resources ( 1461936–200621
NABA annual count data ( 1031999–201027
Georgia Dept. of Natural Resources ( 771994–200115
Global Biodiversity Information Facility (GBIF) 751974–200449
North Carolina Natural Heritage Program ( 691989–200321
The Lepidopterists’ Society ( 501973–200825
All Taxa Biodiversity Inventory (ATBI) ( 461936–20074
Carolina Butterfly Society (CBS) 442001–20095
Carolinaleps 412007–20099
Washington Area Butterfly Club 2920071
Oklahoma Leps 212005–20095 212007–20099
Table 2. Summary of literature referencing the distribution of Speyeria diana (adapted from Wells and Tonkyn 2014).
Table 2. Summary of literature referencing the distribution of Speyeria diana (adapted from Wells and Tonkyn 2014).
ReferenceLocationDate of Record(s)Description
Cramer & Stoll 1775Jamestown, Virginia1775holotype; male described by Pieter Cramer
Blatchley 1859Vanderburgh County, Indiana1850sfirst record from Indiana, most northern record
Edwards 1864Kanawha, West Virginia20–31 August 1864first description of female, took over 30 specimens
Edwards 1874Coalburgh, West VirginiaAugust, September 1873description of rearing Argynnis larvae
Aaron 1877Tennessee/North Carolina1877populations are ample along Blue Ridge
Kentucky1877locally abundant populations
Strecker 1878 1878West Virginia, Georgia, Kentucky, Tennessee, Arkansas
Thomas 1878Kentucky, Arkansas, southern Illinois1878common in Kentucky & Arkansas
Fisher 1881Illinois1880present in southern Illinois
Holland 1883Salem, North Carolina1858–1861described as “first pinned female specimen”
Edwards 1884southern Ohio1880sfirst description in Ohio
Hulst 1885Waynesville, North Carolina1882locally abundant populations
Warren Springs, North Carolina1882very common along the French Broad River
Blatchley 1886Evansville, Indianaearly 1900slocally abundant populations
French 1886eastern United States1886W. Virginia to Georgia, Southern Ohio to Illinois, Kentucky, Tennessee, Arkansas
Hine 1887a, bMedina County, Ohio9 August 1887single worn male, northernmost record in OH
Kingsley 1888Virginia1887Argynnis diana is described as the handsomest insect found in the United States
Scudder 1889southeast United States1880sSemnopsyche diana; an inhabitant of hilly country of the south, 38th parallel of latitude, taken as far west as Missouri and “Arkansaw”
Skinner & Aaron 1889Pennsylvania1880sstray individual found in Pennsylvania
Dixey 1890eastern United States1889description of Argynnis diana wing spot pattern
Blatchley 1891Illinois1890sfemale specimen from northern Danville, IL
Skinner 1896southern Illinois1890sDiana specimens from southern Illinois are larger than those further east
Holland 1898southern United States1890sin two Virginias and Carolinas, northern Georgia, Tennessee, Kentucky, occasionally in southern Ohio and Indiana, and in Missouri and Arkansas; the most magnificent and splendid species of the genus
Snyder 1900Clay County, Illinois1900northern limit of S. diana in Illinois
Strecker 1900Missouri1853pair captured in copula, very early female
Maynard 1901 habitat is West Virginia to Georgia, southern Ohio to Illinois, Tennessee, and Arkansas
Sell 1916Greene County, Missouri22 August 1900southeast of Springfield
Smyth 1916southeast United States1880–1916Asheville, Brevard, North Carolina, Caesar’s Head, South Carolina, Montgomery, Washington and Giles Counties, Virginia
Wood 1916Camp Craig, VirginiaAugust 1914describes female color variation
Murrill 1919Virginia1919Poverty Valley
Holland 1931 1930sThe Virginias and Carolinas, northern GA Tennessee, Kentucky, occasionally in southern OH, Indiana, and in Missouri and Arkansas
Knobel 1931Hope, Arkansas1930from Mrs. Louise Knobel
Kite 1934Taney County, Missouri31 July 1925male and female reported
Clark 1937Virginia1930sranges from Bath County, Virginia to FL east almost to tidewater, and west to Illinois and Arkansas
Clark & Williams 1937Virginialate 1800s–1935Bath, Alleghany, Giles, Bland, Dickenson, Smyth, Patrick, Montgomery & Washington Counties
Allen 1941West Virginia1940Pocahontas County, west to Kanawha and Lincoln Counties; abundant in Jefferson NF (Monroe County), Babcock State Park (Fayette County), and Fork Creek Wildlife Management Area (Boone County)
Chermock 1942Conestee Falls, North Carolinasummer 1941southern. Ohio and West Virginia, through the Appalachian mountains into Georgia and South Carolina, most abundant in mountains south of Great Smoky Mountains National Park
Bock 1949Cincinnati, Ohio1947author collects hundreds of specimens from North Carolina mountains; gone from Indiana and Ohio
Clark & Clark 1951Southern Illinoisearly 1900s
Chesterfield County, Virginia1930last known county record
Northampton County, Virginia1930last known county record
Klots 1951Brevard, North Carolina1950in large numbers along roadsides; Chiefly in mountains and piedmont, W. Virginia s. to Georgia, w. to southern Ohio, Indiana, Missouri, and Arkansas
Mather & Mather 1958Madison Parish, Louisiana1958record is a stray individual
Evans 1959Smoky Mountains of TennesseeSeptember 1957identification of an unknown S. diana larva
Curtis & Boscoe 1962Buncombe County, North Carolina27 June 1962collecting record near Asheville
Hovanitz 1963Salem, Roanoke County, Virginia13 June 1937comprehensive distribution data
Ross & Lambremont 1963Louisiana1950sstray record from Mather & Mather 1958
Masters 1968Newton County, Missouri1960slocally very common
Masters & Masters 1969Perry County, Indiana15 July 1962last record known from Indiana
Shull & Badger 1971Indiana1971no longer resident in Indiana
Harris 1972Georgia1972summarizes historic reports from White, Union, Fannin, Habersham, Rabun Counties
Irwin & Downey 1973Vermilion County, Illinois20 August 1960female, last known Illinois record
Southern Illinois1880Illinois natural history survey
Howe 1975 1950sextirpated from type locality, Jamestown
Kentucky, West Virginia1970sspecies is scarce in Kentucky and West
Georgia1970snot uncommon in northern Georgia
Ceasar’s Head, South Carolina1970sstable populations, not uncommon
Nelson 1979Ozark plateau of Oklahoma1969only found in eastern counties
Schowalter & Drees 1980Poverty Hollow, Virginia1973, 1978field-captured and lab-reared S. diana gynandromorphs described in detail
Pyle 1981eastern United States1980shas decreased its range because of forest loss, common in the Great Smoky Mountains
Hammond & McCorkle 1983Virginia & Tennessee1975–1978Appalachian populations are expanding
Opler 1983eastern United States1980ssome populations under decline
Opler & Krizek 1984 1950sextirpated from Virginia Piedmont and coast
1800sextirpated from Ohio River valley
Shuey et al. 1987Cincinnati, Ohio1900s–1930eliminated by deforestation by early 1900s
Shull 1987Indianalate 1800soccurs in mountains and piedmont of West Virginia south to Georgia, west to southern Ohio, Indiana, Missouri, and Arkansas
Watson & Hyatt 1988Tennessee1980sresident species of northeastern Tennessee
Kohen 1989Cumberland, KentuckyJuly 1984aberrant male on milkweed
Cohen & Cohen 1991Bath County, Virginia1990George Washington National Forest
Montgomery County, Virginia1990photograph of pair in copula
Krizek 1991western Virginia11 July 1991males preferred nectar over horse manure
Adams 1992Fannin County, Georgia28 August 1992female netted by Irving Finkelstein
Opler & Malikul 1992eastern United States1992central Appalachians west to Ozarks, formerly Atlantic coastal plain of Va., NC, and Ohio River Valley, rich forested valleys
Skillman & Heppner 1992Coopers Creek WMA Georgia10 June 1988Gynandromorph specimen found in n. GA
Carlton & Nobles 1996Arkansas, Missouri, Oklahoma1819–1995survey of Interior Highlands
Allen 1997West Virginia1997ranges from Virginia and W. Virginia south to northern Georgia and Alabama. A small population persists in Ozark Mountains of Arkansas and Missouri
Ross 1997Coweeta Forest, North Carolina1990, 1996classified as uncommon, 2–5 individuals sighted
Ross 1998Mount Magazine, Arkansas30 June 1993photograph of male, locally abundant
Mount Magazine, Arkansas20 August 1992photograph of female, locally abundant
Glassberg 1999eastern United States1999formerly throughout Ohio River Valley and southeastern Virginia and northwest N.C
Moran & Baldridge 2002Arkansas, Missouri, Oklahoma1997–199922 counties inhabited, Arkansas expanding
Scholtens 2004Oconee County, South Carolina2002present in Sumter National Forest
Cech & Tudor 2005 2000slocally common in mountain colonies, s. W. Virginia to n. GA; also e. AL/KY, Ozarks
Vaughan & Shepherd 2005Red List species profile2005core of species distribution is in the southern Appalachians from central Virgina and W. VA through the mountains to northern Georgia and Alabama. Also in Ozarks of Missouri, Arkansas, and eastern Oklahoma
Adams & Finkelstein 2006Fannin County, Georgia12 October 2006lots of aggregating females flying late
Rudolph et al., 2006Ouachita Mountains, Arkansas1999–2005feeding records by month sites
Spencer 2006Arkansas2006uncommon to locally common in colonies Scattered throughout the Interior Highlands Coastal Plain
Campbell et al., 2007North Carolina17 June 2004at least four males visiting flowering sourwood
Ross 2008Mount Magazine, Arkansas2008description of Mount Magazine State Park
Wells et al., 2010Mount Magazine, Arkansas2009copulating pair photographed
Wells et al., 2011Georgia, North Carolina, Tennessee2009females collected for rearing trial
Table 3. Field-sampled Speyeria diana (2006–2009). Records are provided to the level of county. All voucher specimens are held at the Clemson University Arthropod Collection (adapted from Wells and Tonkyn 2014).
Table 3. Field-sampled Speyeria diana (2006–2009). Records are provided to the level of county. All voucher specimens are held at the Clemson University Arthropod Collection (adapted from Wells and Tonkyn 2014).
StateCountyEcoregion# S. diana (m/f)Survey Dates
ArkansasBentonOzark Plateau7 (7/1)12–14 June 2007, 22–23 June 2009
CarrollOzark Plateau9 (7/2)15–16 June 2007, 23–24 June 2009
BooneOzark Plateau2 (2/0)16 June 2007
FaulknerArkansas River Valley5 (5/0)18–20 June 2006, 20 June 2007, 16 June 2008, 3–6 August 2009
ConwayArkansas River Valley15 (11/4)22 June 2007, 26 June 2008, 5 August 2009
PulaskiArkansas River Valley4 (2/2)28 August 2009
LoganArkansas River Valley37 (29/8)20–24 June 2006, 21–24 June 2007, 1–3 August 2009
MontgomeryOuachita Mountains12 (7/5)31 July 2008, 1–3 September 2009
PolkOuachita Mountains5 (1/4)1–3 September 2009
SalineOuachita Mountains8 (7/1)14 June 2008, 18 June 2009
OklahomaLefloreOuachita Mountains3 (0/3)30 August 2009
GeorgiaFanninBlue Ridge Mountains26 (17/9)12–13 July & 1 August 2006, 12 July 2007, 22 June & 20 July 2008
RabunBlue Ridge Mountains8 (2/6)7 September 2008, 29 August 2009
UnionBlue Ridge Mountains14 (6/8)29 July 2007, 15 June & 5–7 August 2008,
North CarolinaAsheBlue Ridge Mountains4 (4/0)22–23 June 2007
BuncombeBlue Ridge Mountains13 (8/5)27 July 2006, 30 July 2007, 9 August 2008
McDowellBlue Ridge Mountains15 (10/5)9 September 2007, 24 June 2008, 30 June, 11 September 2009
TransylvaniaBlue Ridge Mountains24 (19/5)5 June 2006, 16 July & 5 September 2007, 14 June 2008, 26 June 2009
WataugaBlue Ridge Mountains7 (5/2)30 May & 9 June 2006, 25 July 2008, 19 September 2009
South CarolinaGreenvilleBlue Ridge Escarpment12 (7/5)31 June 2006, 27–29 July 2007, 1 September 2008, 8–13 September 2009
TennesseeBlountGreat Smoky Mountains42 (33/9)1–26 June 2007, 1–28 June & 20–29 August 2008, 1–15 September 2009
SevierGreat Smoky Mountains33 (25/8)1–26 June 2007, 26–29 June 2008, 5 June-26 September 2009
CarterAppalachian Mountains57 (35/22)5–9 June & 5–11 July 2006, 30–31 May 2007, 29–30 August 2008
SullivanAppalachian Mountains36 (25/11)13–16 July 2006, 20–22 July 2007, 5 August, 18–20 September 2009
VirginiaMontgomeryAppalachian Mountains21 (14/7)3–7 July 2007, 2–4 July 2008
Table 4. Elevation plus the 19 bioclimate variables from the WorldClim dataset (Hijmans et al., 2005) collapsed into groups of highly correlated variables (Pearson’s correlation coefficient, r ≥ ±0.70), and their corresponding contribution to the Maxent model. The ten variables kept in the final model are bold and highlighted in grey. The community climate system model (CCCM) and model for interdisciplinary research on climate (MIROC) global circulation models are shown under representative concentration pathways (RCPs) 4.5 (low) and 8.5 (high), as predicted by the Intergovernmetnal Panel on Climate Change (IPCC) 5th report on climate. AVG—average; AUC—area under curve.
Table 4. Elevation plus the 19 bioclimate variables from the WorldClim dataset (Hijmans et al., 2005) collapsed into groups of highly correlated variables (Pearson’s correlation coefficient, r ≥ ±0.70), and their corresponding contribution to the Maxent model. The ten variables kept in the final model are bold and highlighted in grey. The community climate system model (CCCM) and model for interdisciplinary research on climate (MIROC) global circulation models are shown under representative concentration pathways (RCPs) 4.5 (low) and 8.5 (high), as predicted by the Intergovernmetnal Panel on Climate Change (IPCC) 5th report on climate. AVG—average; AUC—area under curve.
Bioclimate VariablesAbbreviation% Contribution
Annual Mean TemperatureBio
Max Temperature of Warmest MonthBio
Min Temperature of Coldest MonthBio 63.936.320.12.63.310.4
Mean Temperature of Wettest QuarterBio 814.
Mean Temperature of Driest QuarterBio 915.55.110.330.219.825.0
Mean Temperature of Warmest QuarterBio
Mean Temperature of Coldest QuarterBio 110.812.511.
Precipitation of Wettest MonthBio
Precipitation SeasonalityBio
Precipitation of Wettest QuarterBio
Precipitation of Warmest QuarterBio
Precipitation of Driest MonthBio
Precipitation of Driest QuarterBio
Precipitation of Coldest QuarterBio
Isothermality (BIO 2/BIO 7) (*100)Bio 311.
Temperature Seasonality (standard deviation *100)Bio
Mean Diurnal Range (Mean of monthly (max temp—min temp))Bio
Temperature Annual Range (BIO 5–BIO 6)Bio
Annual PrecipitationBio 1222.313.417.922.915.919.4
AUC 0.860.960.910.870.860.87

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Wells, C.N.; Tonkyn, D. Changes in the Geographic Distribution of the Diana Fritillary (Speyeria diana: Nymphalidae) under Forecasted Predictions of Climate Change. Insects 2018, 9, 94.

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Wells CN, Tonkyn D. Changes in the Geographic Distribution of the Diana Fritillary (Speyeria diana: Nymphalidae) under Forecasted Predictions of Climate Change. Insects. 2018; 9(3):94.

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Wells, Carrie N., and David Tonkyn. 2018. "Changes in the Geographic Distribution of the Diana Fritillary (Speyeria diana: Nymphalidae) under Forecasted Predictions of Climate Change" Insects 9, no. 3: 94.

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